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Record W2215408146 · doi:10.25336/p6bc99

Does It Pay to Migrate? The Canadian Evidence

2008· article· en· W2215408146 on OpenAlexaffvenueabout
Young-Deok Shin, Bali Ram

Bibliographic record

VenueCanadian Studies in Population · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsStatistics Canada
Fundersnot available
KeywordsCensusGeographySocioeconomicsDemographic economicsDemographyPopulationEconomicsSociology

Abstract

fetched live from OpenAlex

An analysis of the 1991 and 1996 Census data indicated that on average people who moved out of economically less affluent provinces showed higher incomes than those who were left behind. However, persons who moved out of wealthier provinces did not do as well as those who stayed. In fact, their incomes were lower than non-migrants. According to the 1996 Census, for example, the age-education adjusted income of migrants from Ontario, Alberta, and British Columbia was about 10 to 13% lower than non-migrants in those provinces, whereas the corresponding income was about 7 to 13% higher for migrants from Atlantic Provinces. Similarly, people who moved into economically less resourceful provinces had higher incomes than non-migrants, while inmigrants into affluent provinces did worse than those who stayed in those provinces.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.035
Threshold uncertainty score0.182

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.007
Science and technology studies0.0040.004
Scholarly communication0.0040.002
Open science0.0030.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0260.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.102
GPT teacher head0.362
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2008
Admission routes3
Has abstractyes

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